{"id":"W2948058356","doi":"10.1177/0361198119852339","title":"Analysis of Visual Scanning Patterns Comparing Drivers of Simulated L2 and L0 Systems","year":2019,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Driving simulator; Anticipation (artificial intelligence); Fixation (population genetics); Eye movement; Perception; Hazard; Simulation; Transport engineering; Computer science; Engineering; Psychology; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003451,0.0001833182,0.0007869783,0.002509252,0.0002027178,0.00005854131,0.0005411195,0.0001692778,0.001931332],"category_scores_gemma":[0.00006860241,0.0001488631,0.0004345694,0.002508871,0.0003459954,0.0003886851,0.000007046213,0.001223309,0.00001720131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001181735,"about_ca_system_score_gemma":0.0001743284,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01428195,"about_ca_topic_score_gemma":0.008336741,"domain_scores_codex":[0.9936081,0.001360551,0.0018499,0.0003351024,0.002359406,0.0004869302],"domain_scores_gemma":[0.994005,0.001252835,0.0009772214,0.0003861234,0.003149381,0.000229452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001179614,0.0002248467,0.9537265,0.0002791907,0.001522987,0.00001854506,0.009419109,0.02840422,0.002472779,0.001671192,0.0002242124,0.0008567966],"study_design_scores_gemma":[0.001632876,0.0004984977,0.9738962,0.0003802555,0.0002642805,3.904298e-7,0.01228999,0.009980012,0.0003044711,0.00003718947,0.0005953497,0.000120497],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968133,0.0001016777,0.001032504,0.0002159282,0.0006121274,0.0006600416,0.00007310481,0.00001611011,0.0004752145],"genre_scores_gemma":[0.9989042,0.0001298155,0.00006603831,0.00001026302,0.00004042871,0.00001114237,0.00003402889,0.00002810269,0.0007759432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02016968,"threshold_uncertainty_score":0.9989811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09287558601276863,"score_gpt":0.4527014246131917,"score_spread":0.3598258386004231,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}